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Found 128 results.

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Tree Species - Sentinel-2 - Germany, 2016

The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2016 at a spatial resolution of 10 meters. The map depicts the distribution of ten tree species groups derived from multi-temporal optical Sentinel-2 data. The input features explicitly incorporate phenological information to capture seasonal vegetation dynamics relevant for species discrimination. A total of over 100,000 training and test samples were compiled from publicly accessible sources, including urban tree inventories, Google Earth Pro, Google Street View, and field observations. The final product was created by majority-voting on annual XGBoost Sentinel-2 tree species classifications (2016–2024) and filtering with forest structure data. If no clear majority vote was achieved, the class uncertain was assigned. The Tree Species Germany 2016 product achieves an overall F1-score of 0.95. For the dominant species pine, spruce, beech, and oak, class-wise F1-scores range from 0.92 to 0.99, while F1-scores for other widespread species such as birch, alder, larch, Douglas fir, fir, and other deciduous species range from 0.85 to 0.96. The product provides a consistent, high-resolution, and up-to-date representation of tree species distribution across Germany. Its transferable, cost-efficient, and repeatable methodology enables reliable large-scale forest monitoring and offers a valuable basis for assessing spatial patterns and temporal changes in forest composition in the context of ongoing climatic and environmental dynamics.

WMS SL Sentinel-2 NDVI - Sentinel-2 NDVI 2019

Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2019.

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2020

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2020.

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2021

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2021.

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2026

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2026.

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2024

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2024.

WMS SL Sentinel-2 CIR - Sentinel-2 CIR 2026

Sentinel-2 Falschfarbenbild (ColoredInfraRed), Kombination der Spektralkanäle B8 (rot), B4 (grün) und B3 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert die Sentinel-2 Falschfarbenbilder(CIR) des Jahr 2026.

WMS SL Sentinel-2 NDVI - Sentinel-2 NDVI 2020

Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2020.

Sentinel-2 Mosaike rgb und cir Hessen

Sentinel-2 cir- und rgb-Mosaike (10 m Bodenauflösung) in möglichst wolkenfreier Monatsfolge für: 2021-06, 2021-07, 2021-09, 2021-12, 2022-02, 2022-03, 2022-06, 2022-08, 2022-09, 2023-02, 2023-04, 2023-06, 2024-01, 2024-08, 2024-09, 2025-02, 2025-03, 2025-04, 2025-05, 2025-06, 2025-08, 2025-12, 2026-03, 2026-04, 2026-05

PolarLakes - Sentinel-1/2 - Antarctica, Bi-weekly/Annually

The PolarLakes dataset provides bi-weekly observations of supraglacial lakes on Antarctic ice shelves, utilizing imagery from Sentinel-2 and Sentinel-1 to address time series gaps caused by frequent cloud cover. These observations detect the extents of supraglacial lakes with a U-Net model for every two weeks from November to March, with each sensor operating independently before the data is merged. The resulting bi-weekly product reflects the maximum lake extents for the first and second halves of each month. When combined for an entire season, the dataset consolidates all bi-weekly records over these five months, allowing for analysis of the maximum lake extent per season and the frequency of lake formation, which can occur up to ten times (5 months á two weeks). The year indicated in the dataset corresponds to January of the melt season, as this month typically experiences the highest melt rates (e.g., 2023 refers to the season from November 2022 to March 2023). The aggregation of all annual datasets creates a recurrence layer that illustrates the frequency of lake presence throughout the entire observation period, which spans from 2014 to 2024, depending on satellite data availability for each ice shelf. The PolarLakes dataset provides valuable insights into the dynamics of supraglacial lakes and serves as a crucial resource for hydrological and climate modeling.

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